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3. Optimizing Resonator Frequency Stability in Flip-Chip Architectures: A Novel Experimental Design Approach

Author

Li, Yuan, Wang, Tianhui, Hu, Jingjing, Li, Dengfeng, and An, Shuoming

Subjects
Quantum Physics
Abstract

In multi-qubit superconducting systems utilizing flip-chip technology, achieving high accuracy in resonator frequencies is of paramount importance, particularly when multiple resonators share a common Purcell filter with restricted bandwidth. Nevertheless, variations in inter-chip spacing can considerably influence these frequencies. To tackle this issue, we present and experimentally validate the effectiveness of a resonator design. In our design, we etch portions of the metal on the bottom chip that faces the resonator structure on the top chip. This enhanced design substantially improves frequency stability by a factor of over 3.5 compared to the non-optimized design, as evaluated by the root mean square error of a linear fitting of the observed frequency distribution, which is intended to be linear. This advancement is crucial for successful scale-up and achievement of high-fidelity quantum operations.

Published
2023

4. Application of GPS Technology in Missile Measurement and Control

Author

Pan, Wei, Zhang, Chi, Wang, Tianhui, Liang, Ming, Tong, Tang, Ceccarelli, Marco, Series Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, Agrawal, Sunil K., Advisory Editor, Carbone, Giuseppe, editor, and Laribi, Med Amine, editor

Published
2024
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5. Research on the Concept of Missile Cooperative Combat

Author

Pan, Wei, Zhang, Chi, Wang, Tianhui, Liang, Ming, Wang, Gang, Ceccarelli, Marco, Series Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, Agrawal, Sunil K., Advisory Editor, Carbone, Giuseppe, editor, and Laribi, Med Amine, editor

Published
2024
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8. Multi-source adaptive thresholding adaboost with application to virtual metrology.

Author

Xie, Yifan, Wang, Tianhui, Jeong, Myong K., and Lee, Gyeong Taek

Subjects
MACHINE learning, ENGINEERS, SEMICONDUCTOR wafers, METROLOGY, MANUFACTURING processes
Abstract

To maintain high-quality semiconductor wafer production processes, it is necessary to build high-quality virtual metrology (VM) model. Based on the result of the VM, the engineer can monitor and control the specific process. In the manufacturing process, various sources are obtained from sensor equipment. It is important to consider that these source data exhibit varying characteristics during the model construction. However, when all data sources are simply aggregated into a single model, the performance of the overall model may degrade, especially if any of the individual sources contain outliers or noise. To address this issue, we develop a data-fusion model designed to incorporate diverse data sources into a unified multi-source model. In particular, we improve an Adaboost regression algorithm to make it suitable for multi-source data in the field of VM. The algorithm combines the residuals of the models derived from each individual data source and all sources to adaptively adjust the thresholding value, which, in turn, determines whether the predicted values are accurate or not for each weak learner. Extensive practical validations on real-world processing data from semiconductor manufacturers have demonstrated that the proposed method outperforms single learning algorithms and is more robust than all benchmarks. [ABSTRACT FROM AUTHOR]

Published
2024
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9. Multi-source AdaBoost with cross-weight method for virtual metrology in semiconductor manufacturing.

Author

Wang, Tianhui, Baek, Jaeseung, Jeong, Myong-Kee, Seo, Seongho, and Choi, Jaekyung

Subjects
SEMICONDUCTOR manufacturing, MANUFACTURING processes, MACHINE learning, INDUSTRY 4.0, PREDICTION models
Abstract

In the context of Industry 4.0, many production scenarios utilise sensors to monitor manufacturing processes, resulting in massive data that can be leveraged to build machine-learning models to improve production efficiency and quality. In semiconductor manufacturing, where many sensors are employed to monitor the production process, the resulting multi-sensor data poses challenges for constructing a virtual metrology (VM) model to assess wafer quality. Furthermore, building separate prediction models for each sensor can result in a loss of redundancy present in the multi-sensor data. Therefore, this paper proposes a multi-source AdaBoost with a cross-weight sampling method for predicting the critical dimension required in VM. Compared with the existing AdaBoost algorithm in regression, the proposed method adapts well to multi-source data by incorporating a cross-weight sampling distribution. Thus, when updating the sampling weight distribution, the impact of both individual and multi-source data is considered to re-sample high-quality observations. Based on the numerical results from the synthetic datasets and case study in semiconductor manufacturing, the proposed method outperforms benchmark methods. Additionally, owing to the redundancy of multi-sensor data, the proposed method demonstrates robust performance regardless of the noise level in the semiconductor VM data. [ABSTRACT FROM AUTHOR]

Published
2024
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21. True sparse PCA for reducing the number of essential sensors in virtual metrology.

Author

Xie, Yifan, Wang, Tianhui, Jeong, Young-Seon, Tosyali, Ali, and Jeong, Myong K.

Subjects
PRINCIPAL components analysis, DETECTORS, METROLOGY, SEMICONDUCTOR industry
Abstract

In the semiconductor industry, virtual metrology (VM) is a cost-effective and efficient technique for monitoring the processes from one wafer to another. This technique is implemented by generating a predictive model that uses real-time data from equipment sensors in conjunction with measured wafer quality characteristics. Before establishing a prediction model for the VM system, appropriate selection of relevant input variables should be performed to maintain the efficiency of subsequent analyses considering the large dimensionality of the sensor data inputs. However, wafer production processes usually employ multiple sensors, which leads to cost escalations. Herein, we propose a variant of the sparse principal component analysis (PCA) called true sparse PCA (TSPCA). The proposed method uses a small number of input variables in the first few principal components. The main contribution of the proposed TSPCA is reducing the number of essential sensors. Our experimental results demonstrate that compared to the existing sparse PCA methods, the proposed approach can reduce the number of sensors required while explaining an approximately equivalent amount of variance. [ABSTRACT FROM AUTHOR]

Published
2024
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32. Corrigendum to “A novel universal small-molecule detection platform based on antibody-controlled Cas12a switching” [Biosens. Bioelectron. 246 (2024) 115897]

Author

Sun, Tieqiang, primary, Wang, Wen, additional, Wang, Feng, additional, Shen, Weili, additional, Geng, Lu, additional, Zhang, Yiyang, additional, Bi, Meng, additional, Gong, Tingting, additional, Liu, Cong, additional, Guo, Changjiang, additional, Yao, Zhanxin, additional, Wang, Tianhui, additional, and Bai, Jialei, additional

Published
2024
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45. Association Between Depression and Subjective Well-being in older patients with Chronic Diseases in Rural Ethnic Areas of Qiannan, Guizhou: The Mediating Role of Social Support

Author

Li, Zhonglian, primary, Qin, Suxian, additional, Zhu, Yafen, additional, Zhou, Quanxiang, additional, Yi, Aijing, additional, Mo, Caiyun, additional, Gao, Jun, additional, Chen, Juhai, additional, Wang, Tianhui, additional, Feng, Zhanhui, additional, and Mo, Xiangang, additional

Published
2024
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49. Design of Gas Turbine State Data Acquisition Instrument Based on EEMD

Author

Wei, Zhonglin, Liu, Pengyuan, Wang, Feng, Wang, Tianhui, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Wang, Wei, editor, Liu, Xin, editor, Na, Zhenyu, editor, Jia, Min, editor, and Zhang, Baoju, editor

Published
2020
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